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cbpuschmann
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klimacoder_heatpumps_v0.1

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
Model card Files
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Add SetFit model

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Files changed (14) hide show
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  2. 1_Pooling/config.json +10 -0
  3. README.md +1711 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +10 -0
  6. config_setfit.json +8 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. sentencepiece.bpe.model +3 -0
  12. special_tokens_map.json +51 -0
  13. tokenizer.json +3 -0
  14. tokenizer_config.json +55 -0
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+ ---
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: '14. Juli 2022: Ein Dialogversuch Mitten in der Sommerpause veröffentlichen
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+ die beiden für das Heizungsgesetz zuständigen Ministerien, das Bundeswirtschaftsministerium'
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+ - text: 'Falls Sie beim Blick auf die „Bild“-Schlagzeile („Habecks strenges Heizungsverbot
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+ gekippt“) dachten, die Bundesregierung habe sich besonnen, muss ich Sie enttäuschen.
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+ Alles bleibt im Grunde beim Alten. Es gibt nur eine Änderung: Alte Heizungen dürfen
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+ länger in Betrieb bleiben, wenn sie mit klimafreundlichen Gasen laufen. Die Regierung
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+ nennt das „technologieoffen“. Da grüner oder blauer Wasserstoff so schnell nicht
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+ zur Verfügung stehen wird, kommt eigentlich nur Biogas infrage. Wohl dem, der
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+ über einen Misthaufen vor der Haustür verfügt!'
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+ - text: 'Erst mal löst das kaum Debatten aus. Nicht einmal, als die Ampel drei Monate
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+ nach ihrem Start auf Russlands Angriff auf die Ukraine reagiert und stärker Energie
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+ sparen will. Ende März 2022 zieht sie ihre Ziele vor: auf den 1. Januar 2024.
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+ Die 65 Öko-Prozent werde man „jetzt gesetzlich festschreiben“. Aufschrei? Fehlanzeige.
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+ Schlagzeilen machen das gleichzeitig beschlossene Neun-Euro-Ticket und die Senkung
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+ der Mineralölsteuer. In den zuständigen Ministerien für Wirtschaft und Bau beginnt
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+ die Arbeit an einem Heizungsgesetz. Im Stillen.'
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+ - text: 'Doch Widerstände gibt es reichlich, auch in der Koalition. Finanzminister
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+ Christian Lindner (FDP) stimmte dem Gesetz zwar im Kabinett zu. Er tue dies aber
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+ in dem Bewusstsein, dass die Fraktionen im Bundestag „weitere notwendige Änderungen
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+ vornehmen werden“, gab er zu Protokoll. Noch deutlicher wurde FDP-Energiepolitiker
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+ Michael Kruse: Das „Heizungsverbotsgesetz“ überfordere die Menschen, dem könne
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+ er so nicht zustimmen. Ähnlich äußerte sich Unionsfraktionsvize Jens Spahn. „Wir
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+ werden alles dafür tun, dass dieses Gesetz so nicht kommt.“ Jede künftige Wahl
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+ werde nun zu einer „Abstimmung über dieses Gesetz, über diese Überforderung der
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+ Bürgerinnen und Bürger“.'
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+ - text: 'Denn es zieht sich durch alle Grundbedürfnisse, die momentan mit jenem hämischen
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+ Vergnügen aufgekündigt werden, das sich an Zerstörung und Abwertung aufgeilt.
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+ Es gibt hier in den Medien herumgereichte Leute, die die individuelle Mobilität
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+ jenseits des Lastenrades abschaffen und verbieten wollen, und eine politische
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+ Kaste, die ihnen mit Pollern, Steuern und Parkplatzabschaffung entgegenkommt.
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+ Es gibt eine nun schon seit Jahren anhaltende Kampagne gegen das Eigenheim, obwohl
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+ das eigene Haus im Grünen ohne Nachbarn für die große Mehrheit in diesem Land
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+ spätestens mit der Hochzeit die Idealvorstellung ist. Dagegen wird agitiert, obwohl
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+ diese Bauherren die Wirtschaft stützen und selbst Aufgaben übernehmen, für die
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+ der Staat zu unfähig ist. Wer es geschafft hat, wird jetzt mit Heizungsverboten
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+ und teuren Dämmzwängen durch die Hintertür schikaniert, während das erarbeitete
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+ Eigentum massiv an Wert verliert. Die gleiche Kaste in Berlin meint jetzt, man
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+ könnte auch zur 4-Tage-Woche übergehen: Da wäre ich für einen Feldversuch bei
46
+ ihnen, etwa bei Schlüsseldiensten oder Rohrleitungsexperten oder der Müllabfuhr
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+ oder ganz generell bei der Logistik, damit die Arbeitsfernen an den Rechnern in
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+ den Städten einmal ein Gefühl dafür bekommen, was sie diesen Menschen eigentlich
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+ verdanken. Es ist die gleiche Kaste, die Arbeit so belastet, dass sie sich im
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+ Vergleich zu den üppigen Bürgergeld-Geschenken für Migration und Nichtstun kaum
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+ mehr lohnt. Das war in Deutschland früher übrigens auch mal anders, da konnte
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+ man keine Politik gegen die Arbeitnehmer und ihre Bedürfnisse machen.'
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: T-Systems-onsite/cross-en-de-roberta-sentence-transformer
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+ model-index:
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+ - name: SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.6119402985074627
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 3 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:-----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | opposed | <ul><li>'Die geplante flächendeckende Einführung von Wärmepumpen durch das neue Heizungsgesetz stößt auf erhebliche Skepsis, da die Kosten für Hausbesitzer in die Höhe schnellen könnten und die technische Umsetzbarkeit in vielen Altbauten fraglich bleibt. Zudem wird die Frage aufgeworfen, ob der massive staatliche Eingriff in den Heizungsmarkt letztlich zu einer unnötigen Belastung der Bürger führt, ohne einen nennenswerten Effekt auf den globalen CO₂-Ausstoß zu haben.'</li><li>'Aufgewacht aus dem Heizungstraum: Trotz geschärfter Gesetzesinitiativen regt sich massiver Skeptizismus gegen die flächendeckende Einführung von Wärmepumpen! Koste es, was es wolle, bevor blind drauflos gepumpt wird, müssen realistische Lösungen her – alles andere heizt nur Ärger auf!'</li><li>'Wärmepumpen-Wahnsinn auf dem Vormarsch: Die geplante Gesetzesinitiative zur flächendeckenden Einführung von Wärmepumpen sorgt für erhitzte Gemüter und Skepsis in der Bevölkerung. Kritiker warnen vor explodierenden Kosten und unzureichender Infrastruktur, während die Regierung weiterhin auf stur schaltet.'</li></ul> |
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+ | neutral | <ul><li>'Die Bundesregierung plant, den Einbau neuer Gas- und Ölheizungen künftig nur noch in Kombination mit Wärmepumpen zu genehmigen. Bevor das Heizungsverbot in Kraft tritt, müssen Städte und Kommunen zunächst einen Wärmeplan vorlegen, was in Berlin frühestens 2026 erwartet wird.'</li><li>'Die Bundesregierung plant die Einführung eines sogenannten "Heizungsgesetzes", das den Einbau neuer Gas- und Ölheizungen nur noch in Kombination mit Wärmepumpen erlauben soll. Die Umsetzung dieses Gesetzes soll jedoch erst nach Vorlage eines "Wärmeplans" durch die jeweiligen Städte und Kommunen erfolgen, wodurch eine aufschiebende Wirkung entsteht. In einigen Städten, wie Berlin, könnte das Verbot für den Einbau von Gas- und Ölheizungen ohne Wärmepumpen daher erst 2026 in Kraft treten.'</li><li>'In einer jüngsten Gesetzesinitiative wird der Einbau neuer Gas- und Ölheizungen künftig nur noch genehmigt, wenn sie mit Wärmepumpen kombiniert werden. Die Umsetzung des sogenannten "Heizungsgesetzes" soll eine aufschiebende Wirkung haben: Zunächst müssen Städte und Kommunen einen "Wärmeplan" vorlegen, bevor das Heizungsverbot in Kraft tritt. In Berlin könnte dies frühestens im Jahr 2026 der Fall sein.'</li></ul> |
107
+ | supportive | <ul><li>'Die jüngsten Gesetzesinitiativen zur Einführung von Wärmepumpen als Standardheizung haben die Gemüter erhitzt. Kritiker monieren hohe Anschaffungskosten und technische Herausforderungen, während Befürworter den positiven Beitrag dieser Technologie zur Reduzierung der CO2-Emissionen betonen. Angesichts des Klimawandels bleibt die Frage offen, ob diese Maßnahmen ausreichen oder sogar übertrieben sind; sie bilden jedoch einen entscheidenden Schritt in Richtung einer nachhaltigeren Energiepolitik.'</li><li>'Die geplante Einführung der Wärmepumpen als Teil des Heizungsgesetzes stößt auf Kritik, insbesondere hinsichtlich der finanziellen Belastung für Eigenheimbesitzer und der technischen Umsetzbarkeit in Altbauten. Dennoch setzt das Vorhaben ein ermutigendes Signal für den Klimaschutz und die notwendige Transformation des Energiesektors hin zu umweltfreundlicheren Technologien.'</li><li>'Die geplante flächendeckende Einführung von Wärmepumpen durch das neue Heizungsgesetz stößt auf gemischte Reaktionen: Während Kritiker die hohen Anfangsinvestitionen und die technische Umsetzbarkeit in Frage stellen, wird das Gesetz von vielen als notwendiger Schritt hin zu nachhaltigeren und langfristig kostengünstigeren Heizlösungen begrüßt. Besonders in Regionen, in denen Heizsysteme ohnehin erneuert werden müssen, könnte die Initiative einen wichtigen Anstoß für den Umstieg auf umweltfreundlichere Technologien geben.'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.6119 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("cbpuschmann/klimacoder_heatpumps_v0.1")
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+ # Run inference
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+ preds = model("14. Juli 2022: Ein Dialogversuch Mitten in der Sommerpause veröffentlichen die beiden für das Heizungsgesetz zuständigen Ministerien, das Bundeswirtschaftsministerium")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
153
+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 27 | 60.7919 | 195 |
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+
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+ | Label | Training Sample Count |
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+ |:-----------|:----------------------|
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+ | neutral | 363 |
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+ | opposed | 352 |
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+ | supportive | 371 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (3, 3)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
186
+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
193
+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:-----:|:-------------:|:---------------:|
195
+ | 0.0000 | 1 | 0.1865 | - |
196
+ | 0.0020 | 50 | 0.2414 | - |
197
+ | 0.0041 | 100 | 0.2266 | - |
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+ | 0.0061 | 150 | 0.2097 | - |
199
+ | 0.0081 | 200 | 0.1931 | - |
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+ | 0.0102 | 250 | 0.1684 | - |
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+ | 0.0122 | 300 | 0.1417 | - |
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+ | 0.0142 | 350 | 0.0991 | - |
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+ | 0.0163 | 400 | 0.0684 | - |
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+ | 0.0183 | 450 | 0.0349 | - |
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+ | 0.0204 | 500 | 0.023 | - |
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+ | 0.0224 | 550 | 0.0137 | - |
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+ | 0.0244 | 600 | 0.0091 | - |
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+ | 0.0265 | 650 | 0.0066 | - |
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+ | 0.0285 | 700 | 0.0046 | - |
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+ | 0.0305 | 750 | 0.0031 | - |
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+ | 0.0326 | 800 | 0.0024 | - |
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+ | 0.0346 | 850 | 0.002 | - |
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+ | 0.0366 | 900 | 0.0014 | - |
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+ | 0.0387 | 950 | 0.0013 | - |
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+ | 0.0407 | 1000 | 0.001 | - |
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+ | 0.0427 | 1050 | 0.0008 | - |
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+ | 0.0448 | 1100 | 0.0008 | - |
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+ | 0.0468 | 1150 | 0.0006 | - |
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+ | 0.0488 | 1200 | 0.0005 | - |
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+ | 0.0509 | 1250 | 0.0005 | - |
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+ | 0.0529 | 1300 | 0.0004 | - |
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+ | 0.0550 | 1350 | 0.0005 | - |
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+ | 0.0570 | 1400 | 0.0003 | - |
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+ | 0.0590 | 1450 | 0.0003 | - |
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+ | 0.0611 | 1500 | 0.0003 | - |
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+ | 0.0631 | 1550 | 0.0002 | - |
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+ | 0.0651 | 1600 | 0.0002 | - |
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+ | 0.0672 | 1650 | 0.0002 | - |
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+ | 0.0692 | 1700 | 0.0002 | - |
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+ | 0.0712 | 1750 | 0.0001 | - |
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+ | 0.0733 | 1800 | 0.0001 | - |
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+ | 0.0753 | 1850 | 0.0002 | - |
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+ | 0.0773 | 1900 | 0.0003 | - |
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+ | 0.0794 | 1950 | 0.0001 | - |
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+ | 0.0814 | 2000 | 0.0001 | - |
236
+ | 0.0834 | 2050 | 0.0001 | - |
237
+ | 0.0855 | 2100 | 0.0001 | - |
238
+ | 0.0875 | 2150 | 0.0001 | - |
239
+ | 0.0896 | 2200 | 0.0001 | - |
240
+ | 0.0916 | 2250 | 0.0001 | - |
241
+ | 0.0936 | 2300 | 0.0001 | - |
242
+ | 0.0957 | 2350 | 0.0001 | - |
243
+ | 0.0977 | 2400 | 0.0001 | - |
244
+ | 0.0997 | 2450 | 0.0001 | - |
245
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246
+ | 0.1038 | 2550 | 0.0 | - |
247
+ | 0.1058 | 2600 | 0.0 | - |
248
+ | 0.1079 | 2650 | 0.0 | - |
249
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250
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251
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252
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253
+ | 0.1180 | 2900 | 0.0 | - |
254
+ | 0.1201 | 2950 | 0.0 | - |
255
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256
+ | 0.1242 | 3050 | 0.0 | - |
257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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627
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628
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629
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630
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631
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811
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812
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813
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814
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815
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816
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817
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818
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830
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831
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832
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833
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834
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837
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838
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839
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840
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841
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863
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864
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866
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867
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870
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871
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872
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873
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875
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877
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878
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879
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880
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881
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882
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885
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886
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887
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889
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890
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891
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892
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893
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894
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896
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897
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898
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
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976
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977
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978
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979
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980
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981
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982
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983
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984
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985
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986
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987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
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1004
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1005
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1006
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1007
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1008
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1009
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1010
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1011
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1012
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1013
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1014
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1015
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1016
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1017
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1018
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1019
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1020
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1021
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1022
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1023
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1024
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1025
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1026
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1027
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1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
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1091
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1092
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1093
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1094
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1095
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1096
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1097
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1098
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1099
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1100
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1101
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1102
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1103
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1104
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1105
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1106
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1107
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1108
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1109
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1110
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1111
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1112
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1113
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1114
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1115
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1116
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1117
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1118
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1119
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1120
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1121
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1122
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1123
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1124
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1125
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1126
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1127
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1128
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1129
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1130
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1131
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1132
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1133
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1134
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1135
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1136
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1137
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1138
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1139
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1140
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1141
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1142
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1143
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
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1158
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1159
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1160
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1161
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1162
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1163
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1164
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1165
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1166
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1167
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1168
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1169
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1170
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1171
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1172
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1173
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1174
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1175
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1176
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1177
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1178
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1179
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1180
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1181
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1182
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1183
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1184
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1185
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1186
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1187
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1188
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1189
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1190
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1191
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1192
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1193
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1194
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1195
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1196
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1197
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1198
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1199
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1200
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1201
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1202
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1203
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1204
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1205
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1206
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1207
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1208
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1209
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1210
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1211
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1212
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1213
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1214
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1215
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1216
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1217
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1218
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1219
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1220
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1221
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1222
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1223
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1224
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1225
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1226
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1227
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1228
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1229
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1230
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1231
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1232
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1233
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1234
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1235
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1236
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1237
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1238
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1239
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1240
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1241
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1242
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1243
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1244
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1245
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1246
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1247
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1248
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1249
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1250
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1251
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1252
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1253
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1254
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1255
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1256
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1257
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1258
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1259
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1260
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1261
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1262
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1263
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1264
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1265
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1266
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1267
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1268
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1269
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1270
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1271
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1272
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1273
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1274
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1275
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1276
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1277
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1278
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1279
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1280
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1281
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1282
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1283
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1284
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1285
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1286
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1287
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1288
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1289
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1290
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1291
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1292
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1293
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1294
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1295
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1296
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1297
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1298
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1299
+ | 2.2470 | 55200 | 0.0 | - |
1300
+ | 2.2490 | 55250 | 0.0 | - |
1301
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1302
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1303
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1304
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1305
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1306
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1307
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1308
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1309
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1310
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1311
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1312
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1313
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1314
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1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
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1325
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1326
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1327
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1328
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1329
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1330
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1331
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1332
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1333
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1334
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1335
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1336
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1337
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1338
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1339
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1340
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1341
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1342
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1343
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1344
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1345
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1346
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1347
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1348
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1349
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1350
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1351
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1352
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1353
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1354
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1355
+ | 2.3610 | 58000 | 0.0 | - |
1356
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1357
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1358
+ | 2.3671 | 58150 | 0.0 | - |
1359
+ | 2.3691 | 58200 | 0.0 | - |
1360
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1361
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1362
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1363
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1364
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1365
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1366
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1367
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1368
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1369
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1370
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1371
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1372
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1373
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1374
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1375
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1376
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1377
+ | 2.4058 | 59100 | 0.0 | - |
1378
+ | 2.4078 | 59150 | 0.0 | - |
1379
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1669
+
1670
+ ### Framework Versions
1671
+ - Python: 3.11.11
1672
+ - SetFit: 1.1.1
1673
+ - Sentence Transformers: 3.3.1
1674
+ - Transformers: 4.42.2
1675
+ - PyTorch: 2.5.1+cu121
1676
+ - Datasets: 3.2.0
1677
+ - Tokenizers: 0.19.1
1678
+
1679
+ ## Citation
1680
+
1681
+ ### BibTeX
1682
+ ```bibtex
1683
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1684
+ doi = {10.48550/ARXIV.2209.11055},
1685
+ url = {https://arxiv.org/abs/2209.11055},
1686
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1687
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1688
+ title = {Efficient Few-Shot Learning Without Prompts},
1689
+ publisher = {arXiv},
1690
+ year = {2022},
1691
+ copyright = {Creative Commons Attribution 4.0 International}
1692
+ }
1693
+ ```
1694
+
1695
+ <!--
1696
+ ## Glossary
1697
+
1698
+ *Clearly define terms in order to be accessible across audiences.*
1699
+ -->
1700
+
1701
+ <!--
1702
+ ## Model Card Authors
1703
+
1704
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1705
+ -->
1706
+
1707
+ <!--
1708
+ ## Model Card Contact
1709
+
1710
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1711
+ -->
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